---
title: "GoogleGenAIChatGenerator"
id: googlegenaichatgenerator
slug: "/googlegenaichatgenerator"
description: "This component enables chat completion using Google Gemini models through Google Gen AI SDK."
---

# GoogleGenAIChatGenerator

This component enables chat completion using Google Gemini models through Google Gen AI SDK.

<div className="key-value-table">

|                                        |                                                                                                |
| -------------------------------------- | ---------------------------------------------------------------------------------------------- |
| **Most common position in a pipeline** | After a [ChatPromptBuilder](../builders/chatpromptbuilder.mdx)                                             |
| **Mandatory init variables**           | `api_key`: A Google API key. Can be set with `GOOGLE_API_KEY` env var.                         |
| **Mandatory run variables**            | `messages`: A list of [`ChatMessage`](../../concepts/data-classes/chatmessage.mdx)  objects representing the chat        |
| **Output variables**                   | `replies`: A list of alternative replies of the model to the input chat                        |
| **API reference**                      | [Google GenAI](/reference/integrations-google-genai)                                                  |
| **GitHub link**                        | https://github.com/deepset-ai/haystack-core-integrations/tree/main/integrations/google_genai |

</div>

## Overview

`GoogleGenAIChatGenerator` supports `gemini-2.0-flash` (default), `gemini-2.5-pro-exp-03-25`, `gemini-1.5-pro`, and `gemini-1.5-flash` models.

### Tool Support

`GoogleGenAIChatGenerator` supports function calling through the `tools` parameter, which accepts flexible tool configurations:

- **A list of Tool objects**: Pass individual tools as a list
- **A single Toolset**: Pass an entire Toolset directly
- **Mixed Tools and Toolsets**: Combine multiple Toolsets with standalone tools in a single list

This allows you to organize related tools into logical groups while also including standalone tools as needed.

```python
from haystack.tools import Tool, Toolset
from haystack_integrations.components.generators.google_genai import GoogleGenAIChatGenerator

# Create individual tools
weather_tool = Tool(name="weather", description="Get weather info", ...)
news_tool = Tool(name="news", description="Get latest news", ...)

# Group related tools into a toolset
math_toolset = Toolset([add_tool, subtract_tool, multiply_tool])

# Pass mixed tools and toolsets to the generator
generator = GoogleGenAIChatGenerator(
    tools=[math_toolset, weather_tool, news_tool]  # Mix of Toolset and Tool objects
)
```

For more details on working with tools, see the [Tool](../../tools/tool.mdx) and [Toolset](../../tools/toolset.mdx) documentation.

### Streaming

This Generator supports [streaming](guides-to-generators/choosing-the-right-generator.mdx#streaming-support) the tokens from the LLM directly in output. To do so, pass a function to the `streaming_callback` init parameter.

### Authentication

Google Gen AI is compatible with both the Gemini Developer API and the Vertex AI API.

To use this component with the Gemini Developer API and get an API key, visit [Google AI Studio](https://aistudio.google.com/).
To use this component with the Vertex AI API, visit [Google Cloud > Vertex AI](https://cloud.google.com/vertex-ai).

The component uses a `GOOGLE_API_KEY` or `GEMINI_API_KEY` environment variable by default. Otherwise, you can pass an API key at initialization with a [Secret](../../concepts/secret-management.mdx) and `Secret.from_token` static method:

```python
embedder = GoogleGenAITextEmbedder(api_key=Secret.from_token("<your-api-key>"))
```

The following examples show how to use the component with the Gemini Developer API and the Vertex AI API.

#### Gemini Developer API (API Key Authentication)

```python
from haystack_integrations.components.generators.google_genai import GoogleGenAIChatGenerator

## set the environment variable (GOOGLE_API_KEY or GEMINI_API_KEY)
chat_generator = GoogleGenAIChatGenerator()
```

#### Vertex AI (Application Default Credentials)

```python
from haystack_integrations.components.generators.google_genai import GoogleGenAIChatGenerator

## Using Application Default Credentials (requires gcloud auth setup)
chat_generator = GoogleGenAIChatGenerator(
    api="vertex",
    vertex_ai_project="my-project",
    vertex_ai_location="us-central1",
)
```

#### Vertex AI (API Key Authentication)

```python
from haystack_integrations.components.generators.google_genai import GoogleGenAIChatGenerator

## set the environment variable (GOOGLE_API_KEY or GEMINI_API_KEY)
chat_generator = GoogleGenAIChatGenerator(api="vertex")
```

## Usage

To start using this integration, install the package with:

```shell
pip install google-genai-haystack
```

### On its own

```python
from haystack.dataclasses.chat_message import ChatMessage
from haystack_integrations.components.generators.google_genai import GoogleGenAIChatGenerator

## Initialize the chat generator
chat_generator = GoogleGenAIChatGenerator()

## Generate a response
messages = [ChatMessage.from_user("Tell me about movie Shawshank Redemption")]
response = chat_generator.run(messages=messages)
print(response["replies"][0].text)
```

You can also easily use function calls. First, define the function locally and convert into a [Tool](https://www.notion.so/docs/tool):

```python
from typing import Annotated
from haystack.tools import create_tool_from_function

## example function to get the current weather
def get_current_weather(
    location: Annotated[str, "The city for which to get the weather, e.g. 'San Francisco'"] = "Munich",
    unit: Annotated[str, "The unit for the temperature, e.g. 'celsius'"] = "celsius",
) -> str:
    return f"The weather in {location} is sunny. The temperature is 20 {unit}."

tool = create_tool_from_function(get_current_weather)
```

Create a new instance of `GoogleGenAIChatGenerator` to set the tools and a [ToolInvoker](https://www.notion.so/docs/toolinvoker) to invoke the tools.

```python
import os
from haystack_integrations.components.generators.google_genai import GoogleGenAIChatGenerator
from haystack.components.tools import ToolInvoker

os.environ["GOOGLE_API_KEY"] = "<MY_API_KEY>"

genai_chat = GoogleGenAIChatGenerator(tools=[tool])

tool_invoker = ToolInvoker(tools=[tool])
```

And then ask a question:

```python
from haystack.dataclasses import ChatMessage

messages = [ChatMessage.from_user("What is the temperature in celsius in Berlin?")]
res = genai_chat.run(messages=messages)

print(res["replies"][0].tool_calls)

tool_messages = tool_invoker.run(messages=replies)["tool_messages"]
messages = user_message + replies + tool_messages

messages += res["replies"][0] + [ChatMessage.from_function(content=weather, name="get_current_weather")]

final_replies = genai_chat.run(messages=messages)["replies"]
print(final_replies[0].text)
```

#### With Streaming

```python
from haystack.dataclasses.chat_message import ChatMessage
from haystack.dataclasses import StreamingChunk
from haystack_integrations.components.generators.google_genai import GoogleGenAIChatGenerator

def streaming_callback(chunk: StreamingChunk):
    print(chunk.content, end='', flush=True)

## Initialize with streaming callback
chat_generator = GoogleGenAIChatGenerator(
    streaming_callback=streaming_callback
)

## Generate a streaming response
messages = [ChatMessage.from_user("Write a short story")]
response = chat_generator.run(messages=messages)
## Text will stream in real-time through the callback
```

### In a pipeline

```python
import os
from haystack.components.builders import ChatPromptBuilder
from haystack.dataclasses import ChatMessage
from haystack import Pipeline
from haystack_integrations.components.generators.google_genai import GoogleGenAIChatGenerator

## no parameter init, we don't use any runtime template variables
prompt_builder = ChatPromptBuilder()

os.environ["GOOGLE_API_KEY"] = "<MY_API_KEY>"
genai_chat = GoogleGenAIChatGenerator()

pipe = Pipeline()
pipe.add_component("prompt_builder", prompt_builder)
pipe.add_component("genai", genai_chat)
pipe.connect("prompt_builder.prompt", "genai.messages")

location = "Rome"
messages = [ChatMessage.from_user("Tell me briefly about {{location}} history")]
res = pipe.run(data={"prompt_builder": {"template_variables":{"location": location}, "template": messages}})

print(res)
```
